{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 01_relation_similarity_analysis_supplement\n",
    "#\n",
    "# created by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on April 18, 2023\n",
    "# updated by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on April 18, 2023\n",
    "#\n",
    "# 该脚本展示了如何分析训练的关系嵌入.\n",
    "#\n",
    "# 需要的包:\n",
    "#          numpy\n",
    "#          csv\n",
    "#          matplotlib\n",
    "#          sklearn\n",
    "#\n",
    "# 需要的文件:\n",
    "#          ../01-model/ckpts/TransE_l2_All_DRKG_0/All_DRKG_TransE_l2_relation.npy\n",
    "#          ../01-model/ckpts/ComplEx_All_DRKG_0/All_DRKG_ComplEx_relation.npy\n",
    "#          ../01-model/ckpts/RotatE_All_DRKG_0/All_DRKG_RotatE_relation.npy\n",
    "#          ../../data/drkg/relations.tsv\n",
    "#\n",
    "# 源教程链接: https://github.com/gnn4dr/DRKG/blob/master/embedding_analysis/Relation_similarity_analysis.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DRKG Relation Embedding Similarity Analysis\n",
    " \n",
    "这个 notebook 展示了如何分析训练的关系嵌入.\n",
    "\n",
    "在这个例子中, 我们首先加载训练的嵌入向量, 然后将它们映射回原始的关系名, 最后应用下面方法分析它们:\n",
    "\n",
    "- 投射嵌入进入低维空间并可视化它们的分布."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import csv\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "from matplotlib import colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p ./result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Relation ID Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of relations: 107\n"
     ]
    }
   ],
   "source": [
    "rel2id = {}\n",
    "id2rel = {}\n",
    "\n",
    "with open(\"../../data/drkg/relations.tsv\", newline = '', encoding = 'utf-8') as csvfile:\n",
    "    reader = csv.DictReader(csvfile, delimiter='\\t', fieldnames=['id','rel'])\n",
    "    for row_val in reader:\n",
    "        id = row_val['id']\n",
    "        relation = row_val['rel']\n",
    "\n",
    "        rel2id[relation] = int(id)\n",
    "        id2rel[int(id)] = relation\n",
    "\n",
    "print(\"Number of relations: {}\".format(len(rel2id)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treatment relation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "treatment_list = [\n",
    "'DRUGBANK::treats::Compound:Disease',\n",
    "'GNBR::T::Compound:Disease',\n",
    "'Hetionet::CtD::Compound:Disease'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DRUGBANK::treats::Compound:Disease',\n",
       " 'GNBR::T::Compound:Disease',\n",
       " 'Hetionet::CtD::Compound:Disease']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatment_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Relation Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(107, 400) (107, 400) (107, 200)\n"
     ]
    }
   ],
   "source": [
    "TransE_l2_rel_emb = np.load('../01-model/ckpts/TransE_l2_All_DRKG_0/All_DRKG_TransE_l2_relation.npy')\n",
    "ComplEx_rel_emb = np.load('../01-model/ckpts/ComplEx_All_DRKG_0/All_DRKG_ComplEx_relation.npy')\n",
    "RotatE_rel_emb = np.load('../01-model/ckpts/RotatE_All_DRKG_0/All_DRKG_RotatE_relation.npy')\n",
    "print(TransE_l2_rel_emb.shape, ComplEx_rel_emb.shape, RotatE_rel_emb.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Relation Embedding Clustering\n",
    "\n",
    "这里我们使用 t-SNE 将关系嵌入降维, 然后可视化它们的分布."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将关系按照源数据集分类\n",
    "dataset_id = {}\n",
    "for rel_name, i in rel2id.items():\n",
    "    rel_key = rel_name.split('::')[0]\n",
    "    if dataset_id.get(rel_key, None) is None:\n",
    "        dataset_id[rel_key] = []\n",
    "    dataset_id[rel_key].append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "加载之前保存好的 embedded.\n"
     ]
    }
   ],
   "source": [
    "# 降维并转置, 降维的结果每次都不同\n",
    "try:\n",
    "    X_TransE_l2_embedded = np.load('./result/relation/X_TransE_l2_embedded.npy')\n",
    "    X_ComplEx_embedded = np.load('./result/relation/X_ComplEx_embedded.npy')\n",
    "    X_RotatE_embedded = np.load('./result/relation/X_RotatE_embedded.npy')\n",
    "    print(\"加载之前保存好的 embedded.\")\n",
    "except Exception as e :\n",
    "    print(\"重新计算 embedded.\")\n",
    "    X_TransE_l2_embedded = TSNE(n_components=2).fit_transform(TransE_l2_rel_emb).T\n",
    "    X_ComplEx_embedded = TSNE(n_components=2).fit_transform(ComplEx_rel_emb).T\n",
    "    X_RotatE_embedded = TSNE(n_components=2).fit_transform(RotatE_rel_emb).T\n",
    "    np.save('./result/relation/X_TransE_l2_embedded.npy', X_TransE_l2_embedded)\n",
    "    np.save('./result/relation/X_ComplEx_embedded.npy', X_ComplEx_embedded)\n",
    "    np.save('./result/relation/X_RotatE_embedded.npy', X_RotatE_embedded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pair-wise Relation Embedding Cosine Similarity\n",
    "\n",
    "我们使用余弦距离计算成对的嵌入相似度, 然后输出最相似的 10 对."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算余弦相似度\n",
    "similarity_TransE_l2 = sklearn.metrics.pairwise.cosine_similarity(TransE_l2_rel_emb)\n",
    "similarity_ComplEx = sklearn.metrics.pairwise.cosine_similarity(ComplEx_rel_emb)\n",
    "similarity_RotatE = sklearn.metrics.pairwise.cosine_similarity(RotatE_rel_emb)\n",
    "similaritys = [similarity_TransE_l2, similarity_ComplEx, similarity_RotatE]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后, 我们绘制了成对相似度得分分布的直方图."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_similarity(similarity):\n",
    "    # 将相似度矩阵平铺\n",
    "    similarity = similarity.flatten()\n",
    "    \n",
    "    # 清除 self-compare 和 dup-compare\n",
    "    s = similarity < 0.99\n",
    "    s = np.unique(similarity[s])\n",
    "    print(f\"s.shape: {s.shape}.\")\n",
    "    print(f\"min: {s[0]:.3f}, max: {s[-1]:.3f}.\")\n",
    "    print(f\"> 0.90: {len(s[s>0.90])/s.shape[0]*100:.0f}%, >0.50: {len(s[s>0.50])/s.shape[0]*100:.0f}%\")\n",
    "    return s, s.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist(dist, ax):\n",
    "    # N is the count in each bin, bins is the lower-limit of the bin\n",
    "    N, bins, patches = ax.hist(dist)\n",
    "    \n",
    "    '''\n",
    "    # We'll color code by height, but you could use any scalar\n",
    "    fracs = N / N.max()\n",
    "    \n",
    "    # we need to normalize the data to 0..1 for the full range of the colormap\n",
    "    norm = colors.Normalize(fracs.min(), fracs.max())\n",
    "    \n",
    "    # Now, we'll loop through our objects and set the color of each accordingly\n",
    "    for thisfrac, thispatch in zip(fracs, patches):\n",
    "        color = plt.cm.viridis(norm(thisfrac))\n",
    "        thispatch.set_facecolor(color)\n",
    "    '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s.shape: (5669,).\n",
      "min: -0.774, max: 0.978.\n",
      "> 0.90: 0%, >0.50: 5%\n",
      "s.shape: (5670,).\n",
      "min: -0.208, max: 0.908.\n",
      "> 0.90: 0%, >0.50: 1%\n",
      "s.shape: (5671,).\n",
      "min: -0.241, max: 0.233.\n",
      "> 0.90: 0%, >0.50: 0%\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制，不画 treatments\n",
    "plt.figure(figsize = (12, 9))\n",
    "    \n",
    "# TransE_l2\n",
    "plt.subplot(231)\n",
    "plt.title('TransE')\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_TransE_l2_embedded[0][val], X_TransE_l2_embedded[1][val], '.', label=key)\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "\n",
    "# ComplEx\n",
    "plt.subplot(232)\n",
    "plt.title('ComplEx')\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_ComplEx_embedded[0][val], X_ComplEx_embedded[1][val], '.', label=key)\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "\n",
    "# RotatE\n",
    "plt.subplot(233)\n",
    "plt.title('RotatE')\n",
    "for key, val in dataset_id.items():\n",
    "    val = np.asarray(val, dtype=int)\n",
    "    plt.plot(X_RotatE_embedded[0][val], X_RotatE_embedded[1][val], '.', label=key)\n",
    "\n",
    "plt.gca().get_xaxis().set_visible(False)\n",
    "plt.gca().get_yaxis().set_visible(False)\n",
    "\n",
    "# TransE_l2\n",
    "ax_b = plt.subplot(234)\n",
    "plt.title('TransE')\n",
    "\n",
    "plt.xlabel('Cosine similarity')\n",
    "plt.ylabel('Percent of relation pairs')\n",
    "results = plot_similarity(similaritys[0])\n",
    "plot_hist(results[0], ax_b)\n",
    "ax_b.yaxis.set_major_formatter(PercentFormatter(results[1]))\n",
    "\n",
    "# ComplEx\n",
    "ax_c = plt.subplot(235)\n",
    "plt.title('ComplEx')\n",
    "\n",
    "plt.xlabel('Cosine similarity')\n",
    "plt.ylabel('Percent of relation pairs')\n",
    "results = plot_similarity(similaritys[1])\n",
    "plot_hist(results[0], ax_c)\n",
    "ax_c.yaxis.set_major_formatter(PercentFormatter(results[1]))\n",
    "\n",
    "# RotatE\n",
    "ax_d = plt.subplot(236)\n",
    "plt.title('RotatE')\n",
    "\n",
    "plt.xlabel('Cosine similarity')\n",
    "plt.ylabel('Percent of relation pairs')\n",
    "results = plot_similarity(similaritys[2])\n",
    "plot_hist(results[0], ax_d)\n",
    "ax_d.yaxis.set_major_formatter(PercentFormatter(results[1]))\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('./result/relation/relation-flow-chart.svg', bbox_inches='tight', format='svg')\n",
    "plt.savefig('./result/relation/relation-flow-chart.jpg', bbox_inches='tight', format='jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
